1. Field of the Invention
The present invention relates to an image generation apparatus and an image generation method for aiding detection of an abnormality candidate by reading a medical image. The present invention also relates to a program that causes a computer to execute the image generation method.
2. Description of the Related Art
In the field of medicine, two or more medical images of the same patient radiographed at different times have been compared for detecting an abnormality based on a difference between the images and for discussing therapeutic measures by understanding the progression or remission of a disease, for example.
A small abnormal pattern such as that indicative of lung cancer at an early stage is especially easy to miss. Therefore, Japanese Unexamined Patent Publication No. 2002-158923, for example, describes a method for detecting a candidate of such an abnormality. In this method, subtraction processing or the like is carried out between corresponding pixels in two medical images radiographed at different times for finding a difference. In a temporal subtraction image representing the difference, an area having pixels of a predetermined value or larger, or an area having a characteristic shape such as a circular shape is detected as the candidate.
However, in two target images radiographed at different times, positions of the subject are different due to a change in posture of the patient at the time of radiography, for example. Therefore, in order to obtain a subtraction image, positional matching is generally carried out between the images to be processed. U.S. Pat. No. 5,359,513 and U.S. Patent Application Publication No. 20010048757 describe methods for such positional matching, for example. As one of such methods, at least one of two images of the same patient is subjected to global position matching (a linear transform such as an affine transform) including rotation, translation, and enlargement or reduction. Local position matching is also carried out according to non-linear transformation processing (warping processing such as a non-linear transform adopting curve fitting using a quadratic polynomial) based on a relationship between corresponding positions obtained by template matching on local areas. In addition, global position matching and local position matching may be carried out in combination. Furthermore, positional matching is further carried out in a local area having a large positional difference and the vicinity thereof.
These methods are used for positional matching between two-dimensionally projected images. Therefore, in the case where a large three-dimensional positional difference (leaning forward or backward or twisting sideways) is observed due to a posture change of the patient in images, positional matching is not carried out accurately. Therefore, an artifact is generated in a subtraction image, which leads to inaccurate detection of an abnormality. Furthermore, in the case where a patient does not have any image radiographed in the past, no image is used for comparison. Therefore, the methods using a subtraction image cannot be used, regardless of effectiveness of the methods for detecting an abnormal pattern such as that indicative of lung cancer, which tends to be missed.
For this reason, an apparatus for detecting a candidate of an abnormality area has been proposed in U.S. Patent Application Publication No. 20030210813, for example. As “teacher data” in this apparatus are used images in a database storing normal structure images of a subject and shapes of anatomic characteristics extracted from the images. Based on the teacher data, statistical models of normal structures of a subject (a shape change model, a texture change model, a correlation model between shape and texture) are generated, and a normal structure image corresponding to an input medical image is artificially generated based on the models. The candidate of an abnormality area is then detected based on the normal structure image and the input medical image.
The method of artificially generating the normal structure image is effective for detecting a small abnormality area that tends to be missed, even in the case where a large three-dimensional positional change is observed in images of a patient due to a posture change of the patient or in the case where no medical image radiographed in the past is available. However, in computer-aided diagnosis (CAD) regarding the chest, a detected candidate of lung cancer is generally divided into regions of interest, and each of the regions is subjected to lung cancer diagnosis. Therefore, generating an artificial image of the entire chest is not efficient.
Furthermore, characteristics appearing in images vary between the cases of presence and absence of structures such as ribs in the images. Therefore, in the case where an artificial image of the entire chest is generated, the image may have a characteristic that is partially different from that of an original image.
Consequently, accurate generation of an artificial image is desired regarding a region of interest where an abnormality has been detected, rather than generation of an artificial image regarding the entire chest.